Currently, there are three common ways that users use to find desired products on e-commerce websites: through browsing product categories at the websites, through clicking advertisements related to the websites, and performing searches at the websites. Product categories refer to product classifications and are divided into the front-end and the back-end portions. The front-end portion is usually used for UI (user interface) display, the back-end is usually used for product management, and the mapping of relationships between items of the front-end and items of the back-end may be described by rules. Currently, the category systems at websites are expressed as tree structures, with each parent product category representing a node in the tree and having one or more product subcategories, but each subcategory having only one parent product category. Typically, a user interacting with the front-end system may traverse through the different categories of the tree structure by starting with the category at the highest level and clicking through to the subcategories at each subsequent level of the tree. Therefore, as the user traverses from the top to the bottom of the tree structure, the range of products included in each subsequent subcategory becomes increasingly smaller because the product information included in each subcategory becomes more refined.
The following is an example of a process by which a user may typically browse through product categories at a website: products are grouped into categories, and the categories at the highest level of the tree structure (besides the root of the tree) are first grouped. The categories of the group are then ranked based on, for example, the user's interest in each category. The categories are displayed based on their respective ranking. When the user wishes to browse for a desired product, the user may click on one of the displayed categories and browse the product information associated with the selected category and also the subcategories of the selected category. The user may click on one of the subcategories to browse the product information associated with the selected subcategory and also the subcategories of the selected subcategory and repeat traversing through the category system until the user locates the desired product information. For example, a piece of product information may describe a product that is for sale at the e-commerce website. A user would likely need to be familiar with the category system in order to use a typical style of category system navigation technique to find a desired product. The other typical ways of a user finding a desired product at a website includes through clicking on advertisements related to the websites, which selectively promotes an individual product or a particular seller of products at the website, and performing searches at the websites, which requires a user to submit a search query, do not require a user to be familiar with a category system and therefore have become popular ways for search for products.
Intelligent navigation through the category system became available to improve the technique of traversing through the category system (e.g., by reducing a user's searching time and the number of user clicks). Initially, e-commerce websites used the category product quantity navigation form of intelligent navigation. Category product quantity navigation means that after the user has entered keywords, the sorting order of the recommended categories is determined by the quantity of the relevant products under the categories, and then the categories are displayed level by level. Under this technique of category product quantity navigation, which utilizes text matching, an overwhelming number of product categories would be returned to the user because each product category could have several products that match the user's keywords. However, a large number of returned product categories may be confusing for a user and the user might not be able to determine which of the displayed categories includes products that are desirable to the user. For example, in a search for a certain mobile phone handset model number, the first recommended category is “digital accessories,” because the quantity of products under the “digital accessories” category far exceeds the quantity of products under a “handset” category, but it is actually the products under the “handset” category that is closer to the user's search intentions than the products of the “digital accessories” category. Therefore, displaying a product category based primarily on the number of products under a category that matches the user's entered keywords is not always desirable.
One solution to target the previously described problem is to determine a category correlation score for each category based on historical category click actions. The technique is sometimes called the category click navigation technique. Then, the categories are ranked and displayed dynamically according to their computed correlation scores (and the categories with relatively low correlation scores may be hidden). However, this navigation technique still fails to do away with the framework of displaying categories starting from the top level of the category system, in which the user is required to click multiple times in order to select more refined categories for screening and selection. Moreover, the processing needed for the described navigation technique requires level-by-level traversal of the category system and also the display portion of the technique is also approached level by level, which may both be time consuming and inefficient to use for a user who wishes to quickly search through the category system.